---
title: "Coronavirus Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)


`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv", stringsAsFactors = FALSE) %>%
  dplyr::mutate(country = ifelse(country == "United Arab Emirates", "UAE", country),
                country = ifelse(country == "Mainland China", "China", country),
                country = ifelse(country == "North Macedonia", "N.Macedonia", country),
                country = trimws(country),
                country = factor(country, levels = unique(country)))


df_daily <- df %>% 
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE),
                   .groups = "drop") %>%
  tidyr::pivot_wider(names_from = type,
                     values_from = total) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active =  confirmed - death - recovered) %>%
  dplyr::mutate(confirmed_cum = cumsum(confirmed),
                death_cum = cumsum(death),
                recovered_cum = cumsum(recovered),
                active_cum = cumsum(active))


df_tree <- df %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases), .groups = "drop") %>%
  dplyr::mutate(type = ifelse(type == "confirmed", "Confirmed", type),
                type = ifelse(type == "recovered", "Recovered", type),
                type = ifelse(type == "death", "Death", type)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total) %>%
  dplyr::mutate(Active = Confirmed - Death - Recovered) %>%
  tidyr::pivot_longer(cols = -country, names_to = "type", values_to = "total")

df_world <- df_tree %>%
  dplyr::group_by(type) %>%
  dplyr::summarise(total = sum(total), .groups = "drop") %>%
  tidyr::pivot_wider(names_from = type, values_from = total)

names(df_world) <- tolower(names(df_world))

```

Summary
=======================================================================
Row
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(value = paste(format(df_world$confirmed, big.mark = ","), "", sep = " "), 
         caption = "Total Confirmed Cases", 
         icon = "fas fa-user-md", 
         color = confirmed_color)
```


### active {.value-box}

```{r}
valueBox(value = paste(format(df_world$active[1], big.mark = ","), " (",
                       round(100 * df_world$active[1] / df_world$confirmed[1], 1), 
                       "%)", sep = ""), 
         caption = "Active Cases", icon = "fas fa-ambulance", 
         color = active_color)
```

### recovered {.value-box}

```{r}
valueBox(value = paste(format(df_world$recovered[1] , big.mark = ","), " (",
                       round(100 * df_world$recovered[1] / df_world$confirmed[1], 1), 
                       "%)", sep = ""), 
         caption = "Recovered Cases", icon = "fas fa-heartbeat", 
         color = recovered_color)
```

### death {.value-box}

```{r}

valueBox(value = paste(format(df_world$death[1] , big.mark = ","), " (",
                       round(100 * df_world$death[1] / df_world$confirmed[1], 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-heart-broken", 
         color = death_color)
```


Row {.tabset}
-----------------------------------------------------------------------

### Cases Distribution by Type (`r  max(df$date)`)

```{r daily_summary}


plotly::plot_ly(
  data = df_tree %>% dplyr::filter(type == "Confirmed"),
  type= "treemap",
  values = ~total,
  labels= ~ country,
  parents=  ~type,
  domain = list(column=0),
  name = "Confirmed",
  textinfo="label+value+percent parent"
) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Active"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=1),
    name = "Active",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Recovered"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=2),
    name = "Recovered",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Death"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=3),
    name = "Death",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::layout(grid=list(columns=4, rows=1))
  

  


```

### Daily Cumulative Cases
    
```{r}
plotly::plot_ly(data = df_daily,
                x = ~ date,
                y = ~ active_cum, 
                name = 'Active', 
                fillcolor = active_color,
                type = 'scatter',
                mode = 'none', 
                stackgroup = 'one') %>%
  plotly::add_trace(y = ~ recovered_cum,
                    name = "Recovered",
                    fillcolor = recovered_color) %>%
  plotly::add_trace(y = ~ death_cum,
                    name = "Death",
                    fillcolor = death_color) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date",
                              type = "date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")

```




### Recovery/Death Ratio

```{r}
df %>% 
  
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 20000) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
                death_rate = death / confirmed) %>% 
  dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
                death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
  plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
                  x = ~ round(100 * death_rate, 1),
                  size = ~  log(confirmed),
                  sizes = c(5, 70),
                  type = 'scatter', mode = 'markers',
                  color = ~ country,
                  marker = list(sizemode = 'diameter' , opacity = 0.5),
                  hoverinfo = 'text',
                  text = ~paste("
", country, 
                                "
 Confirmed Cases: ", confirmed,
                                "
 Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
                                "
 Death Rate: ",  paste(round(100 * death_rate, 1), "%", sep = ""))
                 ) %>%
  plotly::layout(title = "Recovery / Death Ratio (Countries with More than 20,000 Cases)",
    yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
```

### Map

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(dplyr)
library(purrr)
cv_data_for_plot <- df %>% 
  filter(cases > 0) %>% 
  group_by(country,province,lat,long,type) %>% 
  summarise(cases = sum(cases)) %>% 
  mutate(log_cases = 2 * log(cases)) %>% 
  ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red","green"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk( function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(data=cv_data_for_plot.split[[df]],
                 lng=~long, lat=~lat,
#                 label=~as.character(cases),
                 color = ~pal(type),
                 stroke = FALSE,
                 fillOpacity = 0.8,
                 radius = ~log_cases,
                 popup =  leafpop::popupTable(cv_data_for_plot.split[[df]],
                                              feature.id = FALSE,
                                              row.numbers = FALSE,
                                              zcol=c("type","cases","country","province")),
                 group = df,
#                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
                 labelOptions = labelOptions(noHide = F,
                                             direction = 'auto'))
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```

### Data


```{r}
df_rates <- df_tree %>%
  dplyr::filter(type != "Active") %>%
  tidyr::pivot_wider(names_from = "type", values_from = "total") %>%
  dplyr::mutate(recovery_rate = Recovered / Confirmed,
    death_rate = Death / Confirmed) 


bar_chart <- function(label, width = "100%", height = "14px", fill = "#00bfc4", background = NULL) {
  bar <- htmltools::div(style = list(background = fill, width = width, height = height))
  chart <- htmltools::div(style = list(flexGrow = 1, marginLeft = "6px", background = background), bar)
  htmltools::div(style = list(display = "flex", alignItems = "center"), label, chart)
}

reactable::reactable(df_rates,
                     pagination = FALSE,
                     highlight = TRUE,
                     height = 800,
                     sortable = FALSE,
                     borderless = TRUE,
                     defaultPageSize = nrow(df_rates),
                     columns = list(
                       country = reactable::colDef(name = "Country", minWidth = 50, maxWidth = 100),
                       Confirmed = reactable::colDef(name = "Confirmed",  minWidth = 50, maxWidth = 80),
                       Recovered = reactable::colDef(name = "Recovered",  minWidth = 50, maxWidth = 100),
                       Death = reactable::colDef(name = "Death",  minWidth = 50, maxWidth = 100),
                       recovery_rate = reactable::colDef(name = "Recovery Rate",  minWidth = 50, maxWidth = 100,
                                                         cell = function(value) {
                         width <- paste0(value , "%")
                         # Add thousands separators
                         value <- format(value, big.mark = ",")
                         bar_chart(value, width = width, fill = "green")
                       },
                       align = "left"),
                       death_rate = reactable::colDef(name = "Death Rate",  minWidth = 50, maxWidth = 100,
                                                      cell = function(value) {
                         width <- paste0(value , "%")
                         # Add thousands separators
                         value <- format(value, big.mark = ",")
                         bar_chart(value, width = width, fill = "red")
                       },
                       align = "left"))
)

```

### About

**The Coronavirus Dashboard**

This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/RamiKrispin/coronavirus_dashboard)

**Data**

The input data for this dashboard is the [coronavirus](https://github.com/RamiKrispin/coronavirus) R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv)




**Packages**

* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package. 
* Visualization - the [plotly](https://plot.ly/r/) package
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), and [tidyr](https://tidyr.tidyverse.org/)
* Tables - the [DT](https://rstudio.github.io/DT/) package

**Deployment and reproducibly**

The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:

* Fork the dashboard [repository](https://github.com/RamiKrispin/coronavirus_dashboard), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)

For any question or feedback, you can either open an [issue](https://github.com/RamiKrispin/coronavirus_dashboard/issues) or contact me on [Twitter](https://twitter.com/Rami_Krispin).






Test
=======================================================================


Row {data-width=400}
-----------------------------------------------------------------------



### Recovery and Death Rates by Country
    
```{r}
df_summary <-df %>% 
  # dplyr::filter(country != "Others") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 25) %>%
  dplyr::select(country, confirmed, recovered, death) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
         death_rate = death / confirmed)  
df_summary %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            options = list(pageLength = nrow(df_summary), dom = 'tip')) %>%
  DT::formatPercentage("recover_rate", 2) %>%
  DT::formatPercentage("death_rate", 2) 
```



Daily Trend
=======================================================================


Column {data-width=400}
-------------------------------------
    
### New Cases - Top 15 Countries (`r  max(df$date)`)
    
```{r}
max_date <- max(df$date)
df %>% 
  dplyr::filter(type == "confirmed", date == max_date) %>%
  dplyr::group_by(country) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  dplyr::arrange(-total_cases) %>%
  dplyr::mutate(country = factor(country, levels = country)) %>%
  dplyr::ungroup() %>%
  dplyr::top_n(n = 15, wt = total_cases) %>%
  plotly::plot_ly(x = ~ country,
                  y = ~ total_cases,
                  text = ~ total_cases,
                  textposition = 'auto',
                  type = "bar") %>%
  plotly::layout(yaxis = list(title = "Number of Cases"),
                 xaxis = list(title = ""),
                 margin =  list(
                   l = 10,
                   r = 10,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))

```


### Daily New Cases - China vs. Rest of the World
    
```{r}


```
   
Column {data-width=600}
-------------------------------------
   
### Recovery and Death Rates for Countries with at Least 20000 Cases

```{r}
df %>% 
  
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 20000) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
                death_rate = death / confirmed) %>% 
  dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
                death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
  plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
                  x = ~ round(100 * death_rate, 1),
                  size = ~  log(confirmed),
                  sizes = c(5, 70),
                  type = 'scatter', mode = 'markers',
                  color = ~ country,
                  marker = list(sizemode = 'diameter' , opacity = 0.5),
                  hoverinfo = 'text',
                  text = ~paste("
", country, 
                                "
 Confirmed Cases: ", confirmed,
                                "
 Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
                                "
 Death Rate: ",  paste(round(100 * death_rate, 1), "%", sep = ""))
                 ) %>%
  plotly::layout(yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
  
```   
 
### Cases Status Update for `r  max(df$date)`
    
```{r}
daily_summary <- df %>% 
  dplyr::filter(date == max(date)) %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::select(country = country, confirmed, recovered, death)
  
  
  DT::datatable(data = daily_summary,
                rownames = FALSE,
                colnames = c("Country", "Confirmed", "Recovered", "Death"),
                options = list(pageLength = nrow(daily_summary), dom = 'tip'))
```

Data
=======================================================================

```{r}
df %>% 
  dplyr::select(Date = date, Province = province, Country = country, `Case Type` = type, `Number of Cases` = cases) %>%
  DT::datatable(rownames = FALSE,
            options = list(searchHighlight = TRUE, 
                           pageLength = 20), filter = 'top')
```